Model v0.2.3 was created using wind, lag_sst, int_chl, sss for cfin. The models were averaged into climatologies with one climatology per month. Evaluations were compiled from the climatological averages and plotted. Finally, the study area was divided up into three regions, the Mid-Atlantic Bight (MAB), George’s Bank (GBK), and the Gulf of Maine (GOM). Actual versus predicted abundance values were plotted for each region. The mgvc GAMs and the gbm BRTs were run using the CPR dataset(s). The Biomod2 models were run using the ECOMON dataset(s). If the model is an anomaly, all datasets are used.
The ensemble models were created using the biomod2 package. The ensembles consist of BRTs, GAMs, and random forests (RFs). The ensembles were used to model the right whale feeding threshold, with any abundance greater than 10^{4} cfin per \(m^2\) counted as a presence and anything below that threshold counted as an absence.
Figure 1. Monthly climatological ensemble projections of GAMs, BRTs, and random forests (RFs). The climatology was created by averaging together the projections from 2000 to 2017.
The GAM models created with biomod2 were used to model the right whale feeding threshold, with any abundance greater than 10^{4} cfin per \(m^2\) counted as a presence and anything below that threshold counted as an absence.
Figure 2. Monthly climatological GAM projections produced using Biomod2. The climatology was created by averaging together the projections from 2000 to 2017.
The BRT models created with biomod2 were used to model the right whale feeding threshold, with any abundance greater than 10^{4} cfin per \(m^2\) counted as a presence and anything below that threshold counted as an absence.
Figure 3. Monthly climatological BRT projections produced using Biomod2. The climatology was created by averaging together the projections from 2000 to 2017.
The RF models created with biomod2 were used to model the right whale feeding threshold, with any abundance greater than 10^{4} cfin per \(m^2\) counted as a presence and anything below that threshold counted as an absence.
Figure 4. Monthly climatological RF projections produced using Biomod2. The climatology was created by averaging together the projections from 2000 to 2017.
Monthly ensemble Biomod2 projections are displayed below for the months of May, June, July, August, and September.
Figure 5. Ensemble projections for the month of April over 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.
Figure 6. Ensemble projections for the month of May over 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.
Figure 7. Ensemble projections for the month of June over 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.
Figure 8. Ensemble projections for the month of August over 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.
Figure 9. Ensemble projections for the month of September over 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.
Evaluation metrics differed based on the metrics available in each modeling package and compatible with each model object. For the mgcv GAMs, Aikaike’s Information Criterion (AIC), the root mean squared error (RMSE), and the R squared (RSQ) value when comparing the actual and predicted abundances were computed. For the BRTs produced using the gbm package, RMSE and RSQ were computed. For the biomod2 ensembles and GAMs, the area under the receiver operator characteristic curve (AUC) and the true skill statistic (TSS) were computed.
Figure 10. Biomod ensemble evaluations on a monthly time scale using a.) AUC and b.) TSS
Figure 11. Biomod GAM evaluations on a monthly time scale using a.) AUC and b.) TSS
Figure 12. Biomod BRT evaluations on a monthly time scale using a.) AUC and b.) TSS
Figure 13. Biomod RF evaluations on a monthly time scale using a.) AUC and b.) TSS
Figure 14. Biomod GAM variable contributions on a monthly time scale.
Figure 15. Biomod BRT variable contributions on a monthly time scale.
Figure 16. Biomod RF variable contributions on a monthly time scale.
For the biomod ensemble models, the logged actual abundance of cfin was plotted against the predicted probability of suitability. Above the threshold of 10^{4}, the probability of suitability tends to increase. Below the threshold, the probability of suitability tends to be zero.
Figure 17. Actual logged abundance versus predicted probability of suitability for cfin for all 12 months.
The generalized additive models (GAMs) were run using the mgcv package and were used to model cfin abundances.
Figure 18. Monthly climatological GAM projections. The climatology was created by averaging together the projections from 2000 to 2017.
Figure 19. Model evaluations on a monthly time scale using a.) AIC, b.) RMSE, and c.) R2.
The study area was divided into three regions, the MAB, GBK, and GOM. For each region, a climatological average (one point per month), monthly average time series, and annual average time series were computed and the actual versus predicted abundance values were plotted.
Figure 20. Climatological abundance values averaged over three regions, a.) the Mid Atlantic Bight, b.) George’s Bank, and c.) the Gulf of Maine. The predicted values come from the projections.
Figure 21. Abundance values averaged monthly over three regions, a.) the Mid Atlantic Bight, b.) George’s Bank, and c.) the Gulf of Maine. The predicted values come from the projections.
Figure 22. Abundance values averaged annually over three regions, a.) the Mid Atlantic Bight, b.) George’s Bank, and c.) the Gulf of Maine. The predicted values come from the projections.
Actual abundance values were plotted against predicted abundance values.
Figure 23. Actual logged abundance versus logged predicted abundance for cfin for all 12 months.
The boosted regression tree (BRT) models were run using the gbm package and used to model cfin abundances.
Figure 24. Monthly climatological BRT projections. The climatology was created by averaging together the projections from 2000 to 2017.
Figure 25. Model evaluations on a monthly time scale using a.) RMSE and b.) R2.
The study area was divided into three regions, the MAB, GBK, and GOM. For each region, a climatological average (one point per month), monthly average time series, and annual average time series were computed and the actual versus predicted abundance values were plotted.
Figure 26. Climatological abundance values averaged over three regions, a.) the Mid Atlantic Bight, b.) George’s Bank, and c.) the Gulf of Maine. The predicted values come from the projections.
Figure 27. Abundance values averaged monthly over three regions, a.) the Mid Atlantic Bight, b.) George’s Bank, and c.) the Gulf of Maine. The predicted values come from the projections.
Figure 28. Abundance values averaged annually over three regions, a.) the Mid Atlantic Bight, b.) George’s Bank, and c.) the Gulf of Maine. The predicted values come from the projections.
Actual abundance values were plotted against predicted abundance values.
Figure 29. Actual logged abundance versus logged predicted abundance for cfin for all 12 months.